Bilee

🚨BREAKING: You no longer need Bloomberg to analyze stocks. Claude can now run: • Goldman Sachs-style research • Morgan Stanley technicals • Bridgewater risk models • Citadel sector rotation • Two Sigma macro analysis All from one prompt. For free. Here are 10 insane Claude prompts that replace a $2,000/month Bloomberg Terminal. Save this. You'll use it daily.

Semi-automated research assistant for academic work github.com/Galaxy-Dawn/cl…

BREAKING: MIT just mass released their Al library for free. (Links included) I went through these and honestly... this is better than most paid courses I've seen. Here's the full list of books: Foundations 1. Foundations of Machine Learning Core algorithms explained. Theory meets practice. 2. Understanding Deep Learning Neural networks demystified. Visual explanations included. 3. Machine Learning Systems Production-ready architecture. System design principles. Advanced Techniques 4. Algorithms for ML Computational thinking simplified. Decision-making frameworks. 5. Deep Learning The definitive textbook. Covers everything deeply. Reinforcement Learning 6. RL Basics (Sutton & Barto) The classic. Agent training fundamentals. 7. Distributional RL Beyond expected rewards. Advanced theory. 8. Multi-Agent Systems Agents working together. Coordination and competition. 9. Long Game Al Strategic agent design. Future-focused thinking. Ethics & Probability 10. Fairness in ML Bias detection. Responsible Al practices. 11. Probabilistic ML (Part 1 & 2) Links: lnkd.in/gkuXuexa Most people pay thousands for bootcamps that teach half of this. Bookmark it. Start anywhere. Just start. Repost for others Follow for more insights on Al Agents. MIT's books on Al Foundations 1. Foundations of Machine Learning - lnkd.in/gytjT5HC 2. Understanding Deep Learning - lnkd.in/dgcB68Qt 3. Machine Learning Systems - lnkd.in/dkiGZisg Advanced Techniques 4. Algorithms for ML - algorithmsbook.com 5. Deep Learning - lnkd.in/g2efT6DK Reinforcement Learning 6. RL Basics (Sutton & Barto) - lnkd.in/guxqxcZZ 7. Distributional RL - lnkd.in/d4eNP-pe 8. Multi-Agent Systems - marl-book.com 9. Long Game Al - lnkd.in/g-WtzvwX Ethics & Probability 10. Fairness in ML - fairmlbook.org 11. Probabilistic ML (Part 1) - lnkd.in/g-isbdjj 12. Probabilistic ML (Part 2) - lnkd.in/gJE9fy4w

BREAKING: Claude can now research like a Stanford PhD student. Here are 9 insane Claude prompts that turn 40+ research papers into structured literature reviews, knowledge maps, and research gaps in minutes (Save this)

Top 10 Free Finance Courses: From Wharton, Yale, MIT, and more. Get the high-quality PDF with clickable links: Subscribe for free at WildCapital.co 1. Introduction to Corporate Finance - Columbia Learn valuation, capital budgeting, and cost of capital. 🔗 edx.org/learn/corporat… 2. Finance Theory I - Yale Understand capital markets and core financial theory. 🔗 ocw.mit.edu/courses/15-401… 3. Financial Markets - Yale Explore investing, insurance, risk, and financial systems. 🔗coursera.org/learn/financia… 4. Introduction to Financial Accounting - University of Pennsylvania Learn how to read, prepare, and analyze financial statements. 🔗coursera.org/learn/wharton-… 5. Finance for Non-Finance Professionals - Rice University (Coursera) A practical intro to cash flow, valuation, and capital budgeting. 🔗coursera.org/learn/finance-… 6. Finance for Managers - University of Navarra (Coursera) Build operational finance skills for better business decisions. 🔗coursera.org/learn/operatio… 7. Finance for Everyone - University of Michigan Use simple tools to make smarter financial choices. 🔗edx.org/learn/financia… 8. Fintech: Blockchain for Business & Finance - University of Texas Understand blockchain and its real-world financial use cases. 🔗 edx.org/learn/economic… 9. Shaping the Financial World - MIT See how new technologies are transforming finance. 🔗ocw.mit.edu/courses/15-s08… 10. Blockchain and Money - MIT Learn how blockchain is reshaping finance and entrepreneurship. 🔗ocw.mit.edu/courses/15-s12… All courses are either free or free to audit. Use these resources to sharpen your financial skills! P.S. Which finance topic do you want to master next? ♻️ Repost to help others level up their finance knowledge.

my quant friend got fired from Two Sigma sent me his resignation email with one attachment "the only edge that mattered" Variance Drag: Geometric Return ≈ Arithmetic Return - σ²/2 sounds harmless until the numbers hit +30% then -30% ≠ 0% it's -9% ran this on leveraged polymarket strategies 2x leverage with 40% volatility = -16% annual drag you can have 55% win rate and still bleed out watched someone turn $8k into $2.1k in 3 weeks positive expected value on every trade variance just ate them alive the math doesn't care about your edge it cares about your volatility now i track realized vol on every position if σ > 60%, i cut size by half if σ > 80%, i don't enter at all same strategies, lower vol, actual profit monitor vol before it kills your account: t.me/KreoPolyBot?st… edge without vol management = slow death

FROM DUMB TO QUANT 18 months of lock-in and you're at a top-tier firm Only one variable - discipline The stack is free: MIT posted Strang's linear algebra course, Harvard gives away probability theory PDFs, Stanford - optimization While you’re scrolling TikTok, a guy your age is clearing $300K-$500K at Jane Street The choice is yours

> amazon.nl/Quantitative-T… > amazon.nl/Machine-Tradin… > amazon.nl/Richard-C-Grin… > amazon.nl/Edward-Qian-eb… > amazon.nl/John-C-Hull-eb… > amazon.nl/Sheldon-Natenb… > amazon.nl/Practical-Guid… > amazon.nl/Quant-Intervie…

nobody tells you what quantitative analysts actually read to get a $300,000 job it's not on reddit. it's not in any guide full reading list, sorted by where you are: if you're starting out ▸ Chan E.P. Quantitative Trading [1] ▸ Chan E.P. Machine Trading [2] if you're serious about equities ▸ Grinold & Kahn Active Portfolio Management [3] ▸ Qian, Hua & Sorensen Quantitative Equity Portfolio Management [4] if you want derivatives ▸ Hull J.C. Options, Futures, and Other Derivatives [5] ▸ Natenberg S. Option Volatility & Pricing [6] if you have an interview next month ▸ Zhou X. Quantitative Finance Interviews [7] ▸ Joshi M.S. Quant Job Interview Q&A [8]

MIT, Yale, Oxford, Columbia uploaded their entire quant trading courses for free. The same lectures their students pay $200K for. 40 hours of pure math and systematic strategies. Block 2-3 hours this weekend and actually start.

Python notebooks for quantitative finance algorithms github.com/cantaro86/Fina…

This is the exact algorithm serious Polymarket arbitrage bot use to extract risk-free money Math of Prediction Market bots like gabagool22 is Adaptive Fully-Corrective Frank-Wolfe + Bregman Projection Broke down this math in easy words in the article below

People talk a lot about quant firms. But not many people know what each firm specializes on. Here's a cheatsheet to understand what each firm does well. Disclaimer - these are just the ones I know well, some eg: Virtu are missing. The cool part? All of them are very profitable!

【进击的 MM 2：做市商订单簿与订单流】 为什么在买入山寨币后短时间内老是水逆？为什么看似资金巨无霸的做市商接不住 1011 的卖单？为什么 1011 后聊的 mm, 每个都云淡风清的说自己当天没咋亏/反倒赚了？这篇文章将为大家介绍做市商的订单薄与订单流 1，限价订单簿 (Limit Order Book, LOB) 做市商的战场不是 K 线图，而是 LOB 核心概念： 1：深度 (Depth)： 每一层价格上的挂单量。 2：步长 (Tick Size)： 价格变动的最小单位。在高频环境中，步长对排队策略至关重要。 3：价格改进 (Price Improvement)： 当你提供的价格优于当前最优买卖价（NBBO）时，你就为市场创造了价值。 比如现在 BTC bid 是10 万，ask 是11万，要是你以 10.1 万价格出价，你就缩小了 spread, 为市场创造了价值。 我在写文章的时候，想怎么给大家讲这个板块，后来我觉得，干啥都没有直接把真实 oreder book展示一下来的清楚。帮 bg 的好朋友打个广告吧，用 bgb 举例 @xiejiayinBitget 上面就是 bgb 最小步长的订单簿。我们看到最小是 0.001，现在盘口深度只有 1000 多刀，价差控的很小，打到了最小 tick size。同时深度以“圣诞树”形状分布，越远离盘口单量越大，但是当我们把 tick size 调大一点后会发现，深度应该按照喇叭状分布，离盘口越近单量越大，流动性越足，越远摆单越少（1011流动性真空的原因之一） 2，做市商盈利来源：点差(spread) 点差不仅仅是利润，它主要由三部分成本构成： 1：订单处理成本 (Order Processing)： 交易所手续费、硬件延迟、人力。 2：库存风险 (Inventory Risk)： 持有头寸期间价格对自己不利的风险，我们上一集讲了 3：逆向选择成本 (Adverse Selection)： 这是最核心的——你成交时，对方可能掌握了你不知道的信息。也就是说你可能被内幕哥阴了。 点差也有三种：Quoted Spread (报价点差)，Effective Spread (有效点差)，Realized Spread (实现点差)。 报价点差是最好理解的 ask-bid 价差，实现点差是衡量做市商在价格调整后实际留下的利润： 2 x (P_trade – P_futuremid) 这里面包含了未来的中位价，有点像考虑机会成本。 3，订单流 order flow 订单流是做市商收到的下单，这是个很深的 topic，做市商会对订单流做各种操作比如对冲，匹配，改动摆单等等，in order to 管理自己的 book（账本）。这里面有相当多专业概念和操作技巧，甚至还会涉及法律问题，比如 agency trade 不能和 principle trade 做对手方，due to conflict of interest（但是币圈不管这块）。 本文就只介绍订单流毒性 (Order Flow Toxicity) 与VPIN 这两个概念，要是有 mm 老板招我打工我再来给粉丝朋友们更新专业 order flow managing（open to job🥲） 有毒流量 (Toxic Flow)是指来自知情交易者的订单，他们知道价格即将变动，所以造成 realized spread 损失，因为他们通过内幕知道了P_futuremid。所以作为狗庄我们也要当心被有毒的内幕哥阴了。 无毒流量(Noise/Retail Flow)是来自散户或被动调整权重的基金。这是做市商最喜欢的“口粮”。 为了保护自己，做市商的防毒机制会调整报价。一种简单的防止“毒性”的方法是，假设所有主动订单都是有毒的，假设买入，于是 mm立刻下调 reservation price，报价整体下移。这也回答了上一章留下来的问号，为什么我们老是买在高点？因为做市商会风控调整报价。但聪明的你一定会再次提出疑问，如果内幕哥大量买入呢？仗着有信息优势直觉头铁冲击盘口怎么办？是的，这可能是 1011 发生的事，也是资产百亿的大 mm 没接住盘的原因。 核心指标来了：VPIN (Volume-weighted Probability of Informed Trading) VPIN ≈ 当前市场中，做市商被“单向流量持续击中”的概率，出现大量单边压力时 MM inventory 单边累积，Mean reversion 假设失效，此时 mm 会撤单（Pull quotes），暂时不提供流动性来等 order flow 恢复对称。但是万一没等来order flow 恢复对称？或者 order flow 偏移太离谱直接把自己爆仓了？这就是 1011 惨案了。有点想出一集写写交易所1011是如何赚钱的，看看吧 说回正题，VPIN 异常后，MM 会撤单（Pull quotes），或扩大点差（Widen spread），相当于多赚点服务费来弥补价位赔钱，还有缩小 size控制 inventory 累积速度。 本集是我们做市商第一部分故事的结尾，从散户视角来看，传说中狗庄操盘的真相已经被揭露。接下来我会从 mm 角度，介绍一些更“机构”的 topic，大家抓稳。 动漫每集的结尾都会放下集预告，进击的 MM 第三集预告：如果我们进入咒术回战的世界，订单流是“咒力”，报价操作是“术式”，那么下一篇，我们将看看做市中的“领域展开”。 欢迎关注，不要走开

Been spending more time thinking about the quant side of crypto lately. Not trying to reinvent myself overnight — just feel it’s time to understand the market on a deeper, more systematic level. Figuring out where this path takes me, step by step.

@nghilt19411 @EkuboProtocol I’m done w my bag lol 🤣

@bileelee_ @EkuboProtocol can we buy more at this price 🥳🥳

my fren just x4 her port with $EKUBO😳 she bought at $3 - $4, sold at $7, waited for dip back to $3.5 and aped in again. simple af but it works 🧠

. @EkuboProtocol cooking fr. trading volume: $127M day, $3.5B month. fees going psycho, TVL flatlining like it’s plotting. buyback flywheel doing laps — positive loop energy hitting diff. fees pump → buybacks hit → token tightens → more flow comes in. feels like pre-breakout vibes ngl.

@lykhangky @EkuboProtocol Also your fren 🤨

@bileelee_ @EkuboProtocol we all know there is no "fren" here